Towards Training Set Reduction for Bug Triage
This work addresses bug triage for software developers by incrementally enhancing existing machine learning approaches through data reduction.
The authors tackled the problem of large-scale, low-quality training sets in bug triage by proposing a training set reduction method using feature and instance selection, which removed 70% of words and 50% of bug reports while improving accuracy on Eclipse bug data.
Bug triage is an important step in the process of bug fixing. The goal of bug triage is to assign a new-coming bug to the correct potential developer. The existing bug triage approaches are based on machine learning algorithms, which build classifiers from the training sets of bug reports. In practice, these approaches suffer from the large-scale and low-quality training sets. In this paper, we propose the training set reduction with both feature selection and instance selection techniques for bug triage. We combine feature selection with instance selection to improve the accuracy of bug triage. The feature selection algorithm, instance selection algorithm Iterative Case Filter, and their combinations are studied in this paper. We evaluate the training set reduction on the bug data of Eclipse. For the training set, 70% words and 50% bug reports are removed after the training set reduction. The experimental results show that the new and small training sets can provide better accuracy than the original one.